Huge volumes of data flow through a robust Kafka architecture, into several ETLs, receiving, transforming and storing the data. We clearly understood our ETLs’ workflow and our data architecture, from source to destination.

But how much did we know about the way our data makes though our systems? And what about the life long question, is it the end of the day yet?

In this talk I’m going to present to you the design process behind our Data Auditing system, Life Line. From tracking and producing, to analyzing and storing auditing information, using technologies such as Kafka, Avro, Spark, Lambda functions and complex SQL queries. We’re going to cover: * AVRO Audit header * Auditing heart beat - designing your metadata * Designing and optimizing your auditing table - what does this data look like anyway? * Creating an alert based monitoring system * Answering the most important question of all - is it the end of the day yet?

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Most organizations routinely operate their business with complex cloud data architectures that silo applications, users and data. As a result, there is no single source of truth of data for analytics, and most analysis is performed with stale data. To solve these challenges, the lakehouse has emerged as the new standard for data architecture, with the promise to unify data, AI and analytic workloads in one place. In this session, we will cover why the data lakehouse is the next best data warehouse. You will hear from the experts success stories, use cases, and best practices learned from the field and discover how the data lakehouse ingests, stores and governs business-critical data at scale to build a curated data lake for data warehousing, SQL and BI workloads. You will also learn how Databricks SQL can help you lower costs and get started in seconds with instant, elastic SQL serverless compute, and how to empower every analytics engineers and analysts to quickly find and share new insights using their favorite BI and SQL tools, like Fivetran, dbt, Tableau or PowerBI.

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dbt's analytics engineering workflow has been adopted by 11,000+ teams, and quickly become an industry standard for data transformation. This is a great chance to see why.

dbt allows anyone who knows SQL to develop, document, test, and deploy models. With the native, SQL-first integration between Databricks and dbt Cloud, analytics teams can collaborate in the same workspace as data engineers and data scientists to build production-grade data transformation pipelines on the lakehouse.

In this live session, Aaron Steichen, Solutions Architect at dbt Labs will walk you through dbt's workflow, how it works with Databricks, and what it makes possible.

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Drew Banin is the co-founder of dbt Labs and one of the maintainers of dbt Core, the open source standard in data modeling and transformation. In this talk, he will demonstrate an approach to unifying SQL and Python workloads under a single dbt execution graph, illustrating the powerful, flexible nature of dbt running on Databricks.

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dbt has done a great job of building an elegant, common interface between data engineers and data analysts: uniting on SQL. As the data industry evolves, there's plenty of pain and room to grow in building that interface between data scientists and data analysts. There isn't a good answer for when things go wrong in the machine learning arena: should the data analyst own fine-tuning the pre-processing data(think: prepping transformed data even more for machine learning models to better work with the data). Should we increase the SQL surface area to build ML models or should we leave that to non-SQL interfaces(python/scala/etc.)? Does this have to be an either/or future? Whatever the interface evolves into, it must center people, create a low bar and high ceiling, and focus on outcomes and not the mystique of features/tools behind a learning curve.

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If you’ve ever had to delete a set of records for regulatory compliance, update a set of records to fix an issue in the ingestion pipeline, or apply changes in a transaction log to a fact table, you know that row-level operations are becoming critical for modern data lake workflows. This talk will focus on some of the upcoming features in Spark 3.3 that will enable execution of row-level operations and allow Spark to only pass to connectors what rows to delete, update, or insert. As a result, data sources won’t have to provide low-level SQL extensions for Spark and will be able to benefit from a scalable built-in implementation that works across all connectors. The presentation will be useful for data source developers as well as data engineers and analysts interested in performing DELETE, UPDATE, MERGE operations in Spark.

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Productionalizing ML models is hard. In fact, very few ML projects make it to production, and one of the hardest problems is data! Most AI platforms are disconnected from the data platform, making it challenging to keep features constantly updated and available in real-time. Offline/online skew prevents models from being used in real-time or, worse, introduces bugs and biases in production. Building systems to enable real-time inference requires valuable production engineering resources. As a result of these challenges, most ML models do not see the light of day.

Learn how you can simplify production ML using Databricks Feature Store, the first feature store built on the data lakehouse. Data sources for features are drawn from a central data lakehouse, and the feature tables themselves are tables in the lakehouse, accessible in Spark and SQL for both machine learning and analytics use cases. Features, data pipelines, source data, and models can all be co-governed in a central platform. Feature Store is seamlessly integrated with Apache Spark™, enabling automatic lineage tracking, and with MLflow, enabling models to look up feature values at inference time automatically. See these capabilities in action and how you can use it for your ML projects.

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Business Intelligence (BI) tools such as Tableau and Microsoft Power BI are notoriously slow at extracting large query results from traditional data warehouses because they typically fetch the data in a single thread through a SQL endpoint that becomes a data transfer bottleneck. Data analysts can connect their BI tools to Databricks SQL endpoints to query data in tables through an ODBC/JDBC protocol integrated in our Simba drivers. With Cloud Fetch, which we released in Databricks Runtime 8.3 and Simba ODBC 2.6.17 driver, we introduce a new mechanism for fetching data in parallel via cloud storage such as AWS S3 and Azure Data Lake Storage to bring the data faster to BI tools. In our experiments using Cloud Fetch, we observed a 10x speed-up in extract performance due to parallelism.

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This talk is about the implementation of a Data Mesh in a Fozzy Group. In our experience, the biggest bottleneck in transition to Data Mesh is unclear data ownership. This and other issues can be solved with (federated) computational data governance. We will go through the process of building a global data lineage with 200k tables, 40k table replications, and 70k SQL stored procedures. Also, we will cover our lessons from building data product culture with explicit and automated tracking of ownership and data quality. Fozzy Group is a holding company that comprises about 40 different businesses with 60k employees in various domains: retail, banking, insurance, logistics, agriculture, HoReCa, E-Commerce, etc.

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In this talk, we'll review the major milestones that have defined Spatial SQL as the powerful tool for geospatial analytics that it is today.

From the early foundations of the JTS Topology Suite and GEOS and its application on the PostGIS extension for PostgreSQL, to the latest implementation in Spark SQL using libraries such as the CARTO Analytics Toolbox for Databricks, Spatial SQL has been a key component of many geospatial analytics products and solutions, leveraging the computing power of different databases with SQL as lingua franca, allowing easy adoption by data scientists, analysts and engineers.

The latest innovation in this area is the CARTO Spatial Extension for Databricks, which makes the most of the near-unlimited scalability provided by Spark and the cutting-edge geospatial capabilities that CARTO offers.

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Ingesting data from hundreds of different data sources is critical before organizations can execute advanced analytics, data science, and machine learning. Unfortunately, ingesting and unifying this data to create a reliable single source of truth is usually extremely time-consuming and costly. In this session, discover how Databricks simplifies data ingestion, at low latency, with SQL-only ingestion capabilities. We will discuss and demonstrate how you can easily and quickly ingest any data into the lakehouse. The session will also cover newly-released features and tools that make data ingestion even simpler on the Databricks Lakehouse Platform.

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COPY INTO is a popular data ingestion SQL command for Databricks users, especially for customers using Databricks SQL. In this talk, we want to discuss the data ingestion use cases in Databricks and how COPY INTO fits your data ingestion needs. We will discuss a few new COPY INTO features and how to achieve the following use cases: 1. Loading data into a Delta Table incrementally ; 2. Fixing errors in already loaded data and helping you with data cleansing; 3. Evolving your schema over time; 4. Previewing data before ingesting; 5. Loading data from a third party data source. In this session, we will demo the new features, discuss the architecture for the implementation, and how other Databricks features are using COPY INTO under the hood.

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Presto is a widely adopted distributed SQL engine for data lake analytics. With Presto, you can perform ad hoc querying of data in place, which helps solve challenges around time to discover and the amount of time it takes to do ad hoc analysis. Additionally, new features like the disaggregated coordinator, Presto-on-Spark, scan optimizations, a reusable native engine, and a Pinot connector enable added benefits around performance, scale, and ecosystem.

In this session, Philip and Rohan will introduce the Presto technology and share why it’s becoming so popular – in fact, companies like Facebook, Uber, Twitter, Alibaba, and much more use Presto for interactive ad hoc queries, reporting & dashboarding data lake analytics, and much more. We’ll also show a quick demo on getting Presto running in AWS.

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